In a plant system, such as a chemical plant, a petroleum refining plant, or an iron-making plant, it is required to monitor whether transition among various processing states (steps) is performed as planned.
As a technique for monitoring a state of a system, for example, an invariant relation analysis described in PTL 1 is known. In the invariant relation analysis described in PTL 1, a set of correlation functions representing a statistical relation of each of a plurality of pairs of metrics (performance indexes) of the system is set as a correlation model for each period, such as a day of the week or a period of time, during which the system has a specific state. Then, an abnormality of the system is detected by determining whether a newly-acquired metric value conforms to the correlation model associated with a period during which the value is acquired.
FIG. 20 is a diagram illustrating an example of a statistical relation among metrics for each state of a system. In the example of FIG. 20, a relation (Rxy) of each pair of the metrics (“A, C”, “B, C”, “D, E”, “D, F”) changes for each period (T1, T2, . . . ) associated with each state of the system (P1, P2, . . . ). A correlation function (Fxy) is detected for each pair of the metrics, for each period, and a set of correlation functions is set in a correlation model (M1, M2, . . . ).
It is to be noted that, as related arts, PTL 2 discloses a technique in which a correlation model is generated for each of a plurality of pairs of metrics of a system, and an abnormality of the system is detected, in the invariant relation analysis. In addition, PTL 3 discloses a technique in which a larger period to which a correlation model generated for each period can be applied is extracted and is associated with an attribute on a calendar, so that an application schedule of the correlation model is decided, in the invariant relation analysis.